{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "YC4R6nkCp91d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YC4R6nkCp91d",
    "outputId": "1792fab3-0a0c-48c1-c3ce-f07091a06d3e",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# download files\n",
    "!mkdir data\n",
    "!wget \"https://www.dropbox.com/s/948jr9cfs7fgj99/UBER.zip?dl=1\" -O data/UBER.zip\n",
    "!unzip data/UBER.zip -d data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "CuHeyb224pI2",
   "metadata": {
    "id": "CuHeyb224pI2"
   },
   "outputs": [],
   "source": [
    "# set text wrapping\n",
    "from IPython.display import HTML, display\n",
    "\n",
    "\n",
    "def set_css():\n",
    "    display(\n",
    "        HTML(\n",
    "            \"\"\"\n",
    "  <style>\n",
    "    pre {\n",
    "        white-space: pre-wrap;\n",
    "    }\n",
    "  </style>\n",
    "  \"\"\"\n",
    "        )\n",
    "    )\n",
    "\n",
    "\n",
    "get_ipython().events.register(\"pre_run_cell\", set_css)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61",
   "metadata": {
    "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61"
   },
   "outputs": [],
   "source": [
    "from llama_index import download_loader, VectorStoreIndex, ServiceContext\n",
    "from pathlib import Path"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
   "metadata": {
    "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
    "tags": []
   },
   "source": [
    "### Ingest Unstructured Data Through the Unstructured.io Reader\n",
    "\n",
    "Leverage the capabilities of Unstructured.io HTML parsing.\n",
    "Downloaded through LlamaHub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61fed4f1-8cca-4d98-b916-a3184279e256",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "years = [2022, 2021, 2020, 2019]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3",
   "metadata": {
    "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3"
   },
   "outputs": [],
   "source": [
    "UnstructuredReader = download_loader(\"UnstructuredReader\", refresh_cache=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29084a11-f3da-428d-ae17-27b85a365e84",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "29084a11-f3da-428d-ae17-27b85a365e84",
    "outputId": "819e4c48-0bb0-4e22-a84c-3021fbedbc4a"
   },
   "outputs": [],
   "source": [
    "loader = UnstructuredReader()\n",
    "doc_set = {}\n",
    "all_docs = []\n",
    "for year in years:\n",
    "    year_docs = loader.load_data(\n",
    "        file=Path(f\"./data/UBER/UBER_{year}.html\"), split_documents=False\n",
    "    )\n",
    "    # insert year metadata into each year\n",
    "    for d in year_docs:\n",
    "        d.metadata = {\"year\": year}\n",
    "    doc_set[year] = year_docs\n",
    "    all_docs.extend(year_docs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d",
   "metadata": {
    "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d"
   },
   "source": [
    "### Setup a Vector Index for each SEC filing\n",
    "\n",
    "We setup a separate vector index for each SEC filing from 2019-2022.\n",
    "\n",
    "We also optionally initialize a \"global\" index by dumping all files into the vector store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
   "metadata": {
    "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
    "outputId": "bdc1d207-efb2-4f82-bf6c-668cfb7ca98f"
   },
   "outputs": [],
   "source": [
    "# initialize simple vector indices + global vector index\n",
    "# NOTE: don't run this cell if the indices are already loaded!\n",
    "index_set = {}\n",
    "service_context = ServiceContext.from_defaults(chunk_size=512)\n",
    "for year in years:\n",
    "    cur_index = VectorStoreIndex.from_documents(\n",
    "        doc_set[year], service_context=service_context\n",
    "    )\n",
    "    index_set[year] = cur_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3a5e993-99ae-445a-bcae-e2d415d84e34",
   "metadata": {
    "id": "d3a5e993-99ae-445a-bcae-e2d415d84e34"
   },
   "outputs": [],
   "source": [
    "# Load indices from disk\n",
    "index_set = {}\n",
    "for year in years:\n",
    "    index_set[year] = cur_index"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "007a99d7-4f63-44bf-957b-c9f5669bce28",
   "metadata": {
    "id": "007a99d7-4f63-44bf-957b-c9f5669bce28"
   },
   "source": [
    "### Composing a Graph to synthesize answers across 10-K filings (2019-2022)\n",
    "\n",
    "We want our queries to aggregate/synthesize information across *all* 10-K filings. To do this, we define a List index\n",
    "on top of the 4 vector indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6",
   "metadata": {
    "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6"
   },
   "outputs": [],
   "source": [
    "from llama_index import ListIndex, LLMPredictor\n",
    "from langchain import OpenAI\n",
    "from llama_index.indices.composability import ComposableGraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "966388cb-2fe4-4427-ad2e-cea6a0375233",
   "metadata": {
    "id": "966388cb-2fe4-4427-ad2e-cea6a0375233"
   },
   "outputs": [],
   "source": [
    "# set summary text for each doc\n",
    "index_summaries = [f\"UBER 10-k Filing for {year} fiscal year\" for year in years]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22",
   "metadata": {
    "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22"
   },
   "outputs": [],
   "source": [
    "# set number of output tokens\n",
    "llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, max_tokens=512))\n",
    "service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
   "metadata": {
    "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
    "outputId": "e7c99f46-8757-48a2-c84b-cb95a0a3eab9"
   },
   "outputs": [],
   "source": [
    "# define a list index over the vector indices\n",
    "# allows us to synthesize information across each index\n",
    "graph = ComposableGraph.from_indices(\n",
    "    ListIndex,\n",
    "    [index_set[y] for y in years],\n",
    "    index_summaries=index_summaries,\n",
    "    service_context=service_context,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7e2edef1-005f-4733-b64c-5987efa4c88d",
   "metadata": {
    "id": "e46dd052-ab34-4974-b696-16ff217970e0"
   },
   "source": [
    "## Setting up the Chatbot Agent\n",
    "\n",
    "We use Langchain to define the outer chatbot abstraction. We use LlamaIndex as a core Tool within this abstraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c2de0f3-9ec3-4f80-bd36-a509f8bfd4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
    "from langchain.agents import initialize_agent\n",
    "\n",
    "from llama_index.langchain_helpers.agents import (\n",
    "    LlamaToolkit,\n",
    "    create_llama_chat_agent,\n",
    "    IndexToolConfig,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9a6faa1-dc06-4d9c-98b4-06e7ee19c559",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# define a decompose transform\n",
    "from llama_index.indices.query.query_transform.base import DecomposeQueryTransform\n",
    "from llama_index.query_engine.transform_query_engine import TransformQueryEngine\n",
    "\n",
    "decompose_transform = DecomposeQueryTransform(llm_predictor, verbose=True)\n",
    "\n",
    "# define custom query engines\n",
    "custom_query_engines = {}\n",
    "for index in index_set.values():\n",
    "    query_engine = index.as_query_engine()\n",
    "    query_engine = TransformQueryEngine(\n",
    "        query_engine,\n",
    "        query_transform=decompose_transform,\n",
    "        transform_extra_info={\"index_summary\": index.index_struct.summary},\n",
    "    )\n",
    "    custom_query_engines[index.index_id] = query_engine\n",
    "custom_query_engines[graph.root_id] = graph.root_index.as_query_engine(\n",
    "    response_mode=\"tree_summarize\",\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "# construct query engine\n",
    "graph_query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e763ceba-1c54-4abb-9589-c0628d18c5e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# index configs\n",
    "index_configs = []\n",
    "for y in range(2019, 2023):\n",
    "    query_engine = index_set[y].as_query_engine(\n",
    "        similarity_top_k=3,\n",
    "    )\n",
    "    tool_config = IndexToolConfig(\n",
    "        query_engine=query_engine,\n",
    "        name=f\"Vector Index {y}\",\n",
    "        description=f\"useful for when you want to answer queries about the {y} SEC 10-K for Uber\",\n",
    "        tool_kwargs={\"return_direct\": True, \"return_sources\": True},\n",
    "    )\n",
    "    index_configs.append(tool_config)\n",
    "\n",
    "# graph config\n",
    "graph_config = IndexToolConfig(\n",
    "    query_engine=graph_query_engine,\n",
    "    name=f\"Graph Index\",\n",
    "    description=\"useful for when you want to answer queries that require analyzing multiple SEC 10-K documents for Uber.\",\n",
    "    tool_kwargs={\"return_direct\": True, \"return_sources\": True},\n",
    "    return_sources=True,\n",
    ")\n",
    "\n",
    "toolkit = LlamaToolkit(index_configs=index_configs, graph_configs=[graph_config])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c28cd7ee-15ef-4240-aff3-ecc0ba335df2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
    "llm = OpenAI(temperature=0)\n",
    "agent_chain = create_llama_chat_agent(toolkit, llm, memory=memory, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92bd0eaa-73ad-4735-8234-a376fd1b3a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_chain.run(input=\"hi, i am bob\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19ca8476-bfeb-4c88-a0ff-15424e2d91c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_chain.run(input=\"What were some of the biggest risk factors in 2020 for Uber?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05b5cd50-cf28-4ad1-b1c2-067f626bad93",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cross_query_str = \"Compare/contrast the risk factors described in the Uber 10-K across years. Give answer in bullet points.\"\n",
    "\n",
    "response = agent_chain.run(input=cross_query_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54305c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# parse the response w/ sources\n",
    "import json\n",
    "\n",
    "response_json = json.loads(response)\n",
    "print(response)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a066a1c8-2108-447a-ab9a-b82b4f35696e",
   "metadata": {},
   "source": [
    "### Setup Chatbot Loop Within Notebook\n",
    "\n",
    "We'll keep a running loop so that you can converse with the agent. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9dcd609-3921-493e-a4c1-3312dac277d2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# reinitialize agent\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
    "llm = OpenAI(temperature=0)\n",
    "agent_chain = create_llama_chat_agent(\n",
    "    toolkit,\n",
    "    llm,\n",
    "    memory=memory,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edf6b5a9-bac5-4599-93f8-21c2fc44fa4b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "while True:\n",
    "    text_input = input(\"User: \")\n",
    "    response = agent_chain.run(input=text_input)\n",
    "    print(f\"Agent: {response}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50fa7bb1-817c-4ac1-af39-adbcb14139e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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